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Parameter solution of probability integral method under condition of thick loose layer based on SAA-GRNN optimization model
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Jianguo Zhang1, 2, 3, Wenchang Wang1, **, Lianwei Ren4, Youfeng Zou5, Zhilin Dun4
China Safety Science Journal | 2026, 36(5) : 18 - 26
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China Safety Science Journal | 2026, 36(5): 18-26
Safety Technology and Engineering
Parameter solution of probability integral method under condition of thick loose layer based on SAA-GRNN optimization model
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Jianguo Zhang1, 2, 3, Wenchang Wang1, **, Lianwei Ren4, Youfeng Zou5, Zhilin Dun4
Affiliations
  • 1 College of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo Henan 454003, China
  • 2 State Key Laboratory of Coking Coal Resources Green Exploitation, Pingdingshan Henan 467002, China
  • 3 China Pingmei Shenma Holding Group Co., Ltd., Pingdingshan Henan 467002, China
  • 4 School of Civil Engineering, Henan Polytechnic University, Jiaozuo Henan 454003, China
  • 5 School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo Henan 454003, China
Published: 2026-05-28 doi: 10.16265/j.cnki.issn1003-3033.2026.05.0204
Outline
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To address the problems of low accuracy and insufficient adaptability in existing methods for determining the parameters of PIM for predicting surface deformation prediction in goaf areas under thick unconsolidated layers, 36 sets of measured surface movement data from coal mining working faces were selected. The core indicators of mining-geological conditions were screened via Hierarchical Cluster Analysis (HCA), Entropy Weight Method(EWM) and Grey Relational Degree (GRD) analysis. Furthermore, the GRNN model was optimized by integrating K-fold cross-validation with the neighborhood perturbation strategy of SAA, and an SAA-GRNN optimization model was constructed for PIM parameter determination. A case study was conducted using 45 sets of data from coal mining working faces with thick unconsolidated layers in the Jining area. The results show that: seven mining-geological condition indicators can be classified into three categories, and five core input indicators were identified screening, namely mining thickness M, coal seam dip angle α, mining depth H, strike mining degree D3/H, and unconsolidated layer thickness h. The maximum root-mean-squared error (RMSE) of SAA-GRNN model is no more than 0.190 4, the maximum mean absolute error (MAE) is controlled within 0.133 9, the maximum mean absolute percentage error (MAPE) is 0.153 6, and the overall coefficient of determination (R2) is generally above 0.8. Under the same conditions, the prediction errors are greatly reduced compared with those obtained using Back Propagation (BP) neural network and the conventional GRNN model.

simulated annealing algorithm (SAA)  /  generalized regression neural network (GRNN)  /  thick unconsolidated layer  /  probability integral method (PIM)  /  parameter solution
Jianguo Zhang, Wenchang Wang, Lianwei Ren, Youfeng Zou, Zhilin Dun. Parameter solution of probability integral method under condition of thick loose layer based on SAA-GRNN optimization model[J]. China Safety Science Journal, 2026 , 36 (5) : 18 -26 . DOI: 10.16265/j.cnki.issn1003-3033.2026.05.0204
Year 2026 volume 36 Issue 5
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doi: 10.16265/j.cnki.issn1003-3033.2026.05.0204
  • Receive Date:2026-01-11
  • Online Date:2026-06-29
  • Published:2026-05-28
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  • Received:2026-01-11
  • Revised:2026-03-13
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Affiliations
    1 College of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo Henan 454003, China
    2 State Key Laboratory of Coking Coal Resources Green Exploitation, Pingdingshan Henan 467002, China
    3 China Pingmei Shenma Holding Group Co., Ltd., Pingdingshan Henan 467002, China
    4 School of Civil Engineering, Henan Polytechnic University, Jiaozuo Henan 454003, China
    5 School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo Henan 454003, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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种数
Number of
species
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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